Threshold learning from samples drawn from the null hypothesis for the generalized likelihood ratio cusum test
Citation:
C. Hory, A. Kokaram and W. J. Christmas 'Threshold learning from samples drawn from the null hypothesis for the generalized likelihood ratio cusum test' in proceedings of IEEE International Workshop on Machine Learning for Signal Processing, Mystic, Connecticut, USA., 28-30 Sept., 2005, pp 111 - 116.Download Item:
Abstract:
Although optimality of sequential tests for the detection of a change in the parameter of a model has been widely discussed, the test parameter tuning is still an issue. In this communication, we propose a learning strategy to set the threshold of the GLR CUSUM statistics to take a decision with a desired false alarm probability. Only data before the change point are required to perform the learning process. Extensive simulations are performed to assess the validity of the proposed method. The paper is concluded by opening the path to a new approach to multi-modal feature based event detection for video parsing
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Grant Number
Science Foundation Ireland
Author's Homepage:
http://people.tcd.ie/akokaramDescription:
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Author: KOKARAM, ANIL CHRISTOPHER
Sponsor:
Science Foundation IrelandOther Titles:
IEEE International Workshop on Machine Learning for Signal ProcessingType of material:
Conference PaperAvailability:
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Electronic & Electrical EngineeringISSN:
27120Metadata
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